Revolutionizing Biomarker Discovery: Leveraging Generative AI for Bio-Knowledge-Embedded Continuous Space Exploration

TL;DR

This paper introduces a novel AI-driven framework for automatic biomarker discovery that leverages continuous embedding spaces and generative models to improve efficiency and reduce reliance on extensive experiments.

Contribution

It proposes a new biomarker identification method combining data collection, embedding optimization, and generative modeling, advancing automated biomarker discovery.

Findings

Demonstrates efficiency and robustness on real-world datasets

Achieves improved accuracy over traditional methods

Reduces human effort in biomarker selection

Abstract

Biomarker discovery is vital in advancing personalized medicine, offering insights into disease diagnosis, prognosis, and therapeutic efficacy. Traditionally, the identification and validation of biomarkers heavily depend on extensive experiments and statistical analyses. These approaches are time-consuming, demand extensive domain expertise, and are constrained by the complexity of biological systems. These limitations motivate us to ask: Can we automatically identify the effective biomarker subset without substantial human efforts? Inspired by the success of generative AI, we think that the intricate knowledge of biomarker identification can be compressed into a continuous embedding space, thus enhancing the search for better biomarkers. Thus, we propose a new biomarker identification framework with two important modules:1) training data preparation and 2) embedding-optimization-generation. The first module uses a multi-agent system to automatically collect pairs of biomarker subsets and their corresponding prediction accuracy as training data. These data establish a strong knowledge base for biomarker identification. The second module employs an encoder-evaluator-decoder learning paradigm to compress the knowledge of the collected data into a continuous space. Then, it utilizes gradient-based search techniques and autoregressive-based reconstruction to efficiently identify the optimal subset of biomarkers. Finally, we conduct extensive experiments on three real-world datasets to show the efficiency, robustness, and effectiveness of our method.